From fe9ded3bd7d4a8ac04aa6c214405ffcf8ef7b9d2 Mon Sep 17 00:00:00 2001
From: Jan Buethe <jbuethe@amazon.de>
Date: Mon, 29 Apr 2024 15:56:46 +0200
Subject: [PATCH] README related osce fixes
---
dnn/torch/osce/scripts/concatenator.py | 85 ++++++++++++++++++++++++++
1 file changed, 85 insertions(+)
create mode 100644 dnn/torch/osce/scripts/concatenator.py
diff --git a/dnn/torch/osce/scripts/concatenator.py b/dnn/torch/osce/scripts/concatenator.py
new file mode 100644
index 000000000..699d43632
--- /dev/null
+++ b/dnn/torch/osce/scripts/concatenator.py
@@ -0,0 +1,85 @@
+import os
+import argparse
+
+import numpy as np
+from scipy import signal
+from scipy.io import wavfile
+import resampy
+
+
+
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("filelist", type=str, help="file with filenames for concatenation in WAVE format")
+parser.add_argument("target_fs", type=int, help="target sampling rate of concatenated file")
+parser.add_argument("output", type=str, help="binary output file (integer16)")
+parser.add_argument("--basedir", type=str, help="basedir for filenames in filelist, defaults to ./", default="./")
+parser.add_argument("--normalize", action="store_true", help="apply normalization")
+parser.add_argument("--db_max", type=float, help="max DB for random normalization", default=0)
+parser.add_argument("--db_min", type=float, help="min DB for random normalization", default=0)
+parser.add_argument("--verbose", action="store_true")
+
+def read_filelist(basedir, filelist):
+ with open(filelist, "r") as f:
+ files = f.readlines()
+
+ fullfiles = [os.path.join(basedir, f.rstrip('\n')) for f in files if len(f.rstrip('\n')) > 0]
+
+ return fullfiles
+
+def read_wave(file, target_fs):
+ fs, x = wavfile.read(file)
+
+ if fs < target_fs:
+ return None
+ print(f"[read_wave] warning: file {file} will be up-sampled from {fs} to {target_fs} Hz")
+
+ if fs != target_fs:
+ x = resampy.resample(x, fs, target_fs)
+
+ return x.astype(np.float32)
+
+def random_normalize(x, db_min, db_max, max_val=2**15 - 1):
+ db = np.random.uniform(db_min, db_max, 1)
+ m = np.abs(x).max()
+ c = 10**(db/20) * max_val / m
+
+ return c * x
+
+
+def concatenate(filelist : str, output : str, target_fs: int, normalize=True, db_min=0, db_max=0, verbose=False):
+
+ overlap_size = int(40 * target_fs / 8000)
+ overlap_mem = np.zeros(overlap_size, dtype=np.float32)
+ overlap_win1 = (0.5 + 0.5 * np.cos(np.arange(0, overlap_size) * np.pi / overlap_size)).astype(np.float32)
+ overlap_win2 = np.flipud(overlap_win1)
+
+ with open(output, 'wb') as f:
+ for file in filelist:
+ x = read_wave(file, target_fs)
+ if x is None: continue
+
+ if len(x) < 10 * overlap_size:
+ if verbose: print(f"skipping {file}...")
+ continue
+ elif verbose:
+ print(f"processing {file}...")
+
+ if normalize:
+ x = random_normalize(x, db_min, db_max)
+
+ x1 = x[:-overlap_size]
+ x1[:overlap_size] = overlap_win1 * overlap_mem + overlap_win2 * x1[:overlap_size]
+
+ f.write(x1.astype(np.int16).tobytes())
+
+ overlap_mem = x1[-overlap_size]
+
+
+if __name__ == "__main__":
+ args = parser.parse_args()
+
+ filelist = read_filelist(args.basedir, args.filelist)
+
+ concatenate(filelist, args.output, args.target_fs, normalize=args.normalize, db_min=args.db_min, db_max=args.db_max, verbose=args.verbose)
--
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